Sentence examples for by a multinomial logistic from inspiring English sources

The estimated associations given by a multinomial logistic regression are relative risk ratios (RRR).

29 The genotype and allele distributions along with genetic models, ie, dominant, recessive, and overdominant, were analyzed by a multinomial logistic regression using the softwares, SPSS Version 16.0 (Armonk, IBM Corp, NY, USA) and SNPStats (Barcelona, Spain).

The influence of sorafenib dose, PK, or pShift response on sorafenib dose reductions and on the incidence of grade 3 or worse adverse events deemed at least possibly related to sorafenib were explored by a multinomial logistic regression analysis.

We described associations of obesity with intrinsic subtypes by fitting a multinomial logistic regression model.

Odds ratios are calculated by using a multinomial logistic regression model predicting WMH categories with participants who had no history of severe headache and who were in the lowest third for WMH volume as reference group.

Odds ratios are calculated by using a multinomial logistic regression model with participants who had no history of severe headache and who were in lowest third for WMH volume as reference group.

We then assessed the degree to which living in a census tract with higher values of the index increased the risk of heat-related mortality by running a multinomial logistic regression analysis with heat wave days predicting quintiles of the composite index.

Associations between place of death and patient-level variables were assessed by means of a multinomial logistic regression.

To test whether the association differed by pathologic subtype, a multinomial logistic model was used, adjusting for screening round, age at screening, and the remaining variables.

> -wrap-foot> The association of the second type of coping strategy (i.e. foregoing services) with the selected characteristics of respondents was studied by means of a multinomial logistic regression (table 4).

The performance of the two sets of features was evaluated in terms of their ability to characterize multispectral FLIM images by training and testing a multinomial logistic regression classification model (described in the next section) on a subset of 21 out of 58 multispectral FLIM datasets (homogeneous datasets).